TY - GEN
T1 - How deceptive are deceptive visualizations?
T2 - 33rd Annual CHI Conference on Human Factors in Computing Systems, CHI 2015
AU - Pandey, Anshul Vikram
AU - Rall, Katharina
AU - Satterthwaite, Margaret L.
AU - Nov, Oded
AU - Bertini, Enrico
N1 - Publisher Copyright:
© Copyright 2015 ACM.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015/4/18
Y1 - 2015/4/18
N2 - In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions. We create four deceptive visualizations using the selected distortion techniques, and run a crowdsourced user study to identify the deceptiveness of those visualizations. We then present the findings of our study and show how deceptive each of these visual distortion techniques are, and for what kind of questions the misinterpretation occurs. We also analyze individual differences among participants and present the effect of some of those variables on participants' responses. This paper presents a first step in empirically studying deceptive visualizations, and will pave the way for more research in this direction.
AB - In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions. We create four deceptive visualizations using the selected distortion techniques, and run a crowdsourced user study to identify the deceptiveness of those visualizations. We then present the findings of our study and show how deceptive each of these visual distortion techniques are, and for what kind of questions the misinterpretation occurs. We also analyze individual differences among participants and present the effect of some of those variables on participants' responses. This paper presents a first step in empirically studying deceptive visualizations, and will pave the way for more research in this direction.
KW - Deceptive visualization
KW - Empirical analysis
KW - Evaluation
UR - http://www.scopus.com/inward/record.url?scp=84951096830&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951096830&partnerID=8YFLogxK
U2 - 10.1145/2702123.2702608
DO - 10.1145/2702123.2702608
M3 - Conference contribution
AN - SCOPUS:84951096830
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 1469
EP - 1478
BT - CHI 2015 - Proceedings of the 33rd Annual CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 18 April 2015 through 23 April 2015
ER -